Graph Representation of 3D CAD Models for Machining Feature Recognition With Deep Learning

2021 ◽  
Author(s):  
Weijuan Cao ◽  
Trevor Robinson ◽  
Hua Yang ◽  
Flavien Boussuge ◽  
Andrew Colligan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Graph Representation ◽  
3D Cad ◽  
Machining Feature ◽  
Cad Models

Graph Representation of 3D CAD Models for Machining Feature Recognition With Deep Learning

2020 ◽  
Author(s):  
Weijuan Cao ◽  
Trevor Robinson ◽  
Yang Hua ◽  
Flavien Boussuge ◽  
Andrew R. Colligan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Feature Recognition ◽  
Graph Representation ◽  
3D Cad ◽  
Machining Feature ◽  
Cad Models ◽  
Efficient Representation ◽  
Graph Neural Networks ◽  
Interacting Features

Abstract In this paper, the application of deep learning methods to the task of machining feature recognition in CAD models is studied. Four contributions are made: 1. An automatic method to generate large datasets of 3D CAD models is proposed, where each model contains multiple machining features with face labels. 2. A concise and informative graph representation for 3D CAD models is presented. This is shown to be applicable to graph neural networks. 3. The graph representation is compared with voxels on their performance of training deep neural networks to segment 3D CAD models. 4. Experiments are also conducted to evaluate the effectiveness of graph-based deep learning for interacting feature recognition. Results show that the proposed graph representation is a more efficient representation of 3D CAD models than voxels for deep learning. It is also shown that graph neural networks can be used to recognize individual features on the model and also identify complex interacting features.

Deep learning-based framework for Shape Instance Registration on 3D CAD models

2021 ◽  
Author(s):  
Lucas Figueiredo ◽  
Paulo Ivson ◽  
Waldemar Celes
Keyword(s):  
Deep Learning ◽  
3D Cad ◽  
Cad Models

A deep learning approach to the classification of 3D CAD models

2014 ◽  
Vol 15 (2) ◽  
pp. 91-106 ◽  
Author(s):  
Fei-wei Qin ◽  
Lu-ye Li ◽  
Shu-ming Gao ◽  
Xiao-ling Yang ◽  
Xiang Chen
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
3D Cad ◽  
Cad Models

Partial Matching Algorithm of 3D CAD Models Based on the Constraints of Transition Features

2010 ◽  
Vol 97-101 ◽  
pp. 3371-3375
Author(s):  
Kai Xing Zhang ◽  
Shu Sheng Zhang ◽  
Xiao Liang Bai
Keyword(s):  
Feature Recognition ◽  
Graph Isomorphism ◽  
Topological Information ◽  
Machining Features ◽  
New Approach ◽  
Partial Matching ◽  
Mechanical Parts ◽  
3D Cad ◽  
Matching Efficiency ◽  
Cad Models

The CAD models of mechanical parts usually have many blends and chamfers, and the existence of these machining features can greatly change the geometric and topological patterns of the CAD models, but the existing partial matching algorithms cannot match the CAD models which contain machining features such as blends and chamfers. In this paper, a new approach to partial matching based on the constraints of transition features is proposed. Firstly, the transition features are identified by feature recognition, and then these machining features are removed to eliminate the impacts to the geometric and topological information of the CAD models, and the attribute adjacent graph is reconstructed, finally, the sub-graph isomorphism approach is used to achieve the partial matching. Experimental results show that this method can achieve partial matching of CAD models which contain machining features such as blends and chamfers, and the matching efficiency can satisfy the requirement of the engineering retrieval.

scholarly journals Machining feature recognition based on deep neural networks to support tight integration with 3D CAD systems

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Changmo Yeo ◽  
Byung Chul Kim ◽  
Sanguk Cheon ◽  
Jinwon Lee ◽  
Duhwan Mun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Network ◽  
3D Models ◽  
Boundary Representation ◽  
Machining Features ◽  
3D Cad ◽  
Machining Feature ◽  
Tight Integration

AbstractRecently, studies applying deep learning technology to recognize the machining feature of three-dimensional (3D) computer-aided design (CAD) models are increasing. Since the direct utilization of boundary representation (B-rep) models as input data for neural networks in terms of data structure is difficult, B-rep models are generally converted into a voxel, mesh, or point cloud model and used as inputs for neural networks for the application of 3D models to deep learning. However, the model’s resolution decreases during the format conversion of 3D models, causing the loss of some features or difficulties in identifying areas of the converted model corresponding to a specific face of the B-rep model. To solve these problems, this study proposes a method enabling tight integration of a 3D CAD system with a deep neural network using feature descriptors as inputs to neural networks for recognizing machining features. Feature descriptor denotes an explicit representation of the main property items of a face. We constructed 2236 data to train and evaluate the deep neural network. Of these, 1430 were used for training the deep neural network, and 358 were used for validation. And 448 were used to evaluate the performance of the trained deep neural network. In addition, we conducted an experiment to recognize a total of 17 types (16 types of machining features and a non-feature) from the B-rep model, and the types for all 75 test cases were successfully recognized.

scholarly journals Reconfigurable 3D CAD Feature Recognition Supporting Confluent n-Dimensional Topologies and Geometric Filters for Prismatic and Curved Models

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1356
Author(s):  
Juan Pareja-Corcho ◽  
Oscar Betancur-Acosta ◽  
Jorge Posada ◽  
Antonio Tammaro ◽  
Oscar Ruiz-Salguero ◽  
...  
Keyword(s):  
Computer Aided Design ◽  
Feature Recognition ◽  
Graph Isomorphism ◽  
Search Space ◽  
Test Sequence ◽  
3D Cad ◽  
Cad Models ◽  
Aided Design ◽  
Future Work ◽  
Order Sequence

Feature Recognition (FR) in Computer-aided Design (CAD) models is central for Design and Manufacturing. FR is a problem whose computational burden is intractable (NP-hard), given that its underlying task is the detection of graph isomorphism. Until now, compromises have been reached by only using FACE-based geometric information of prismatic CAD models to prune the search domain. Responding to such shortcomings, this manuscript presents an interactive FR method that more aggressively prunes the search space with reconfigurable geometric tests. Unlike previous approaches, our reconfigurable FR addresses curved EDGEs and FACEs. This reconfigurable approach allows enforcing arbitrary confluent topologic and geometric filters, thus handling an expanded scope. The test sequence is itself a graph (i.e., not a linear or total-order sequence). Unlike the existing methods that are FACE-based, the present one permits combinations of topologies whose dimensions are two (SHELL or FACE), one (LOOP or EDGE), or 0 (VERTEX). This system has been implemented in an industrial environment, using icon graphs for the interactive rule configuration. The industrial instancing allows industry based customization and itis faster when compared to topology-based feature recognition. Future work is required in improving the robustness of search conditions, treating the problem of interacting or nested features, and improving the graphic input interface.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

scholarly journals Automatized Evaluation of Students’ CAD Models

2021 ◽  
Vol 11 (4) ◽  
pp. 145
Author(s):  
Nenad Bojcetic ◽  
Filip Valjak ◽  
Dragan Zezelj ◽  
Tomislav Martinec
Keyword(s):  
Computer Aided Design ◽  
Three Dimensional ◽  
Computer Application ◽  
Test Cases ◽  
Cad Model ◽  
Computer Solution ◽  
3D Cad ◽  
Computer Aided ◽  
Cad Models ◽  
Aided Design

The article describes an attempt to address the automatized evaluation of student three-dimensional (3D) computer-aided design (CAD) models. The driving idea was conceptualized under the restraints of the COVID pandemic, driven by the problem of evaluating a large number of student 3D CAD models. The described computer solution can be implemented using any CAD computer application that supports customization. Test cases showed that the proposed solution was valid and could be used to evaluate many students’ 3D CAD models. The computer solution can also be used to help students to better understand how to create a 3D CAD model, thereby complying with the requirements of particular teachers.

scholarly journals Practical method for the fast generation of a CAM model for jet engine parts

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Byung Chul Kim ◽  
Ilhwan Song ◽  
Duhwan Mun
Keyword(s):  
Computer Aided Design ◽  
Feature Recognition ◽  
Practical Method ◽  
Numerical Control ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Jet Engine ◽  
Computer Aided ◽  
Cad Models ◽  
Cam Model

Manufacturers of machine parts operate computerized numerical control (CNC) machine tools to produce parts precisely and accurately. They build computer-aided manufacturing (CAM) models using CAM software to generate code to control these machines from computer-aided design (CAD) models. However, creating a CAM model from CAD models is time-consuming, and is prone to errors because machining operations and their sequences are defined manually. To generate CAM models automatically, feature recognition methods have been studied for a long time. However, since the recognition range is limited, it is challenging to apply the feature recognition methods to parts having a complicated shape such as jet engine parts. Alternatively, this study proposes a practical method for the fast generation of a CAM model from CAD models using shape search. In the proposed method, when an operator selects one machining operation as a source machining operation, shapes having the same machining features are searched in the part, and the source machining operation is copied to the locations of the searched shapes. This is a semi-automatic method, but it can generate CAM models quickly and accurately when there are many identical shapes to be machined. In this study, we demonstrate the usefulness of the proposed method through experiments on an engine block and a jet engine compressor case.

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